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Found 8 papers, 1 papers with code
Streaming Probabilistic PCA for Missing Data with Heteroscedastic Noise
Streaming principal component analysis (PCA) is an integral tool in large-scale machine learning for rapidly estimating low-dimensional subspaces of very high dimensional and high arrival-rate data with missing entries and corrupting noise.
Provable tradeoffs in adversarially robust classification
It is well known that machine learning methods can be vulnerable to adversarially-chosen perturbations of their inputs.
Baseline Estimation of Commercial Building HVAC Fan Power Using Tensor Completion
Commercial building heating, ventilation, and air conditioning (HVAC) systems have been studied for providing ancillary services to power grids via demand response (DR).
Stochastic Gradients for Large-Scale Tensor Decomposition
The stochastic gradient is formed from randomly sampled elements of the tensor and is efficient because it can be computed using the sparse matricized-tensor-times-Khatri-Rao product (MTTKRP) tensor kernel.
Convolutional Analysis Operator Learning: Dependence on Training Data
Convolutional analysis operator learning (CAOL) enables the unsupervised training of (hierarchical) convolutional sparsifying operators or autoencoders from large datasets.
Generalized Canonical Polyadic Tensor Decomposition
Tensor decomposition is a fundamental unsupervised machine learning method in data science, with applications including network analysis and sensor data processing.
Subspace Clustering using Ensembles of $K$-Subspaces
We present a novel geometric approach to the subspace clustering problem that leverages ensembles of the K-subspaces (KSS) algorithm via the evidence accumulation clustering framework.
Towards a Theoretical Analysis of PCA for Heteroscedastic Data
Principal Component Analysis (PCA) is a method for estimating a subspace given noisy samples.
